Scheduling arbitrary-deadline sporadic task systems on multiprocessors

A new algorithm is proposed for scheduling preemptible arbitrary-deadline sporadic task systems upon multiprocessor platforms, with interprocessor migration permitted. This algorithm is based on a task-splitting approach - while most tasks are entirely assigned to specific processors, a few tasks (fewer than the number of processors) may be split across two processors. This algorithm can be used for two distinct purposes: for actually scheduling specific sporadic task systems, and for feasibility analysis. Simulation- based evaluation indicates that this algorithm offers a significant improvement on the ability to schedule arbitrary- deadline sporadic task systems as compared to the contemporary state-of-art. With regard to feasibility analysis, the new algorithm is proved to offer superior performance guarantees in comparison to prior feasibility tests.

The utilization bound of static-priority preemptive partitioned multiprocessor scheduling is 50%

This paper studies static-priority preemptive scheduling on a multiprocessor using partitioned scheduling. We propose a new scheduling algorithm and prove that if the proposed algorithm is used and if less than 50% of the capacity is requested then all deadlines are met. It is known that for every static-priority multiprocessor scheduling algorithm, there is a task set that misses a deadline although the requested capacity is arbitrary close to 50%.

Multiprocessor scheduling with few preemptions

Consider the problem of scheduling a set of periodically arriving tasks on a multiprocessor with the goal of meeting deadlines. Processors are identical and have the same speed. Tasks can be preempted and they can migrate between processors. We propose an algorithm with a utilization bound of 66% and with few preemptions. It can trade a higher utilization bound for more preemption and in doing so it has a utilization bound of 100%.

Collision-free prioritized medium access in the presence of hidden nodes without relying on out-of-band signaling

Consider the problem of sharing a wireless channel between a set of computer nodes. Hidden nodes exist and there is no base station. Each computer node hosts a set of sporadic message streams where a message stream releases messages with real-time deadlines. We propose a collision-free wireless medium access control (MAC) protocol which implements staticpriority scheduling. The MAC protocol allows multiple masters and is fully distributed. It neither relies on synchronized clocks nor out-of-band signaling; it is an adaptation to a wireless channel of the dominance protocol used in the CAN bus. But unlike that protocol, our protocol does not require a node having the ability to receive an incoming bit from the channel while transmitting to the channel. Our protocol has the key feature of not only being prioritized and collision-free but also dealing successfully with hidden nodes. This key feature enables schedulability analysis of sporadic message streams in multihop networks.

Collision-free prioritized medium access control in wireless networks with hidden nodes

We propose a collision-free medium access control (MAC) protocol, which implements static-priority scheduling and works in the presence of hidden nodes. The MAC protocol allows multiple masters and is fully distributed; it is an adaptation to a wireless channel of the dominance protocol used in the CAN bus. But unlike that protocol, our protocol does not require a node having the ability to sense the channel while transmitting to the channel. Our protocol is collision-free even in the presence of hidden nodes and it achieves this without synchronized clocks or out-of-band busy tones. In addition, the protocol is designed to ensure that many non-interfering nodes can transmit in parallel and it functions for both broadcast and unicast transmissions.

Run-time monitoring approach for the shark kernel

Typically common embedded systems are designed with high resource constraints. Static designs are often chosen to address very specific use cases. On contrast, a dynamic design must be used if the system must supply a real-time service where the input may contain factors of indeterminism. Thus, adding new functionality on these systems is often accomplished by higher development time, tests and costs, since new functionality push the system complexity and dynamics to a higher level. Usually, these systems have to adapt themselves to evolving requirements and changing service requests. In this perspective, run-time monitoring of the system behaviour becomes an important requirement, allowing to dynamically capturing the actual scheduling progress and resource utilization. For this to succeed, operating systems need to expose their internal behaviour and state, making it available to the external applications, usually using a run-time monitoring mechanism. However, such mechanism can impose a burden in the system itself if not wisely used. In this paper we explore this problem and propose a framework, which is intended to provide this run-time mechanism whilst achieving code separation, run-time efficiency and flexibility for the final developer.

Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds

Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads’ execution time is also improved, by incorporating an SLA-based redistribution of their VM’s computational power.

Renewable power forecast to scheduling of thermal units

In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

Self-Scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach

This paper is on the self-scheduling for a power producer taking part in day-ahead joint energy and spinning reserve markets and aiming at a short-term coordination of wind power plants with concentrated solar power plants having thermal energy storage. The short-term coordination is formulated as a mixed-integer linear programming problem given as the maximization of profit subjected to technical operation constraints, including the ones related to a transmission line. Probability density functions are used to model the variability of the hourly wind speed and the solar irradiation in regard to a negative correlation. Case studies based on an Iberian Peninsula wind and concentrated solar power plants are presented, providing the optimal energy and spinning reserve for the short-term self-scheduling in order to unveil the coordination benefits and synergies between wind and solar resources. Results and sensitivity analysis are in favour of the coordination, showing an increase on profit, allowing for spinning reserve, reducing the need for curtailment, increasing the transmission line capacity factor. (C) 2014 Elsevier Ltd. All rights reserved.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.